AI app for it and development · no coding needed
Agent code verification and test console
Reduce review time spent re-checking agent claims while catching silent failures before merge.
Made for: Engineering teams using AI coding agents on production repositories
What it does for you
The problem
AI coding agents claim work is complete while features are half-implemented, tests are claimed but never run, and dummy data is substituted, so reviewers cannot trust the output.
What it gives you
Reviewer-approved verification report linked to the original request
What you give it
Agent conversation historyrepository diffsspecificationstest results
Build your own version of Imbue, Vet and more
One app with what these 4 AI tools do, yours to keep and change: Imbue, Vet, TestSprite 2.0, Traycer AI.
Everything these tools do, in one app
- Conversation-history verification Reviews an agent's actions in the context of prior instructions and exchanges to ensure alignment with requested goals.Found in Imbue, Vet
- Silent failure detection Identifies issues like half-implemented features, tests that were claimed but never run, and substituted dummy data.Found in Imbue, Vet
- PR and diff review Flags logic errors, unhandled edge cases, and deviations from stated goals across full pull requests.Found in Imbue, Vet
- Flexible deployment Works with local models, uses existing API keys, runs from the CLI, in CI, or as an agent skill.Found in Imbue, Vet
- Open source Source code is available for inspection and customization.Found in Imbue, Vet
- Zero telemetry Ships with zero telemetry by default for privacy-conscious workflows.Found in Imbue, Vet
- MCP Server integration Connects directly with IDEs and AI coding agents to validate and improve code automatically.Found in TestSprite 2.0
- Automatic test plan generation Generates detailed test plans and cases for both frontend and backend components based on project specifications.Found in TestSprite 2.0
- Test execution and analysis Executes tests with failure analysis and root cause identification to enhance debugging efficiency.Found in TestSprite 2.0
- Continuous feedback loop Refines code with AI coding agents until it meets the original requirements.Found in TestSprite 2.0
- Natural language interaction Eliminates the need for manual prompt writing or test scripting.Found in TestSprite 2.0
- Specification-first workflow Produces phased plans describing what to change, why, and in which order.Found in Traycer AI
- Agent-agnostic execution Allows teams to use their existing AI coding agents rather than switching tools.Found in Traycer AI
- Automated verification against plan Checks proposed diffs against the plan, flags gaps, and detects regressions.Found in Traycer AI
- Context-aware file selection Includes only relevant files during planning and iteration.Found in Traycer AI
- In-editor review and team configuration Supports shared rules and prompt templates for collaboration.Found in Traycer AI
How it works, step by step
- Ingest agent conversation history and prior instructions
- Check agent actions against the requested goals
- Detect half-implemented features and claimed-but-unrun tests
- Flag substituted dummy data and stubbed logic
- Review full pull requests and diffs for logic errors and unhandled edge cases
- Generate frontend and backend test plans from project specifications
- Execute tests and identify root causes of failures
- Feed failures back to the coding agent until requirements are met
- Accept natural language requests without manual test scripting
- Produce phased plans describing what to change, why and in which order
- Work with the team's existing coding agents
- Verify proposed diffs against the plan and flag gaps and regressions
- Select only relevant files during planning and iteration
- Connect to IDEs and agents through an MCP server
- Run from the CLI, in CI or as an agent skill with local models or existing API keys
- Capture corrections and named-owner approval before merge
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Agent code verification and test console with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Agent code verification and test console with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data195 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce review time spent re-checking agent claims while catching silent failures before merge. For engineering teams using AI coding agents on production repositories, convert agent conversation history, repository diffs, specifications and test results into a reviewer-approved verification report linked to the original request. The benefit is a testable hypothesis, measured through verified diffs per review hour and escaped defects after merge; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect agent conversation history, repository diffs, specifications and test results, then follow this sequence: 1. Ingest agent conversation history and prior instructions. 2. Check agent actions against the requested goals. 3. Detect half-implemented features and claimed-but-unrun tests. 4. Review full pull requests and diffs for logic errors and unhandled edge cases. 5. Generate and execute tests, then feed failures back to the coding agent. Resolve uncertain cases with qualified reviewers, approve the reviewer-approved verification report linked to the original request, and measure verified diffs per review hour and escaped defects after merge against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One repository and agent configuration per pilot; final merge and release decisions remain with the engineering team. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve code ownership, source attribution, license compliance and usage permissions. Engineering leads approve substantive changes and merge scope. One repository and agent configuration; final merge and release decisions remain with the engineering team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One repository and agent configuration; final merge and release decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: ingest agent conversation history and prior instructions; check agent actions against the requested goals. Support the third module with operator review: detect half-implemented features and claimed-but-unrun tests. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Team-owned repositories, authorized agent sessions and permitted specifications. Version control, CI systems, IDE extensions and agent APIs. Start with file exchange and validate destination specifications before promising direct merge. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Repository and agent session intake, Verification workbench, Reviewer sign-off and report. Use a project list for repositories and agent sessions, a large central diff and evidence canvas, and a right-hand panel for the original request, plan, test results and comments. Let users compare claimed work against verified work side by side. Display pending, gaps found and approved states. Provide a shareable report link with findings anchored to the relevant file and line. Make the task-specific outcome reviewer-approved verification report linked to the original request visible beside its evidence, review state and value baseline.
